scholarly journals Empirical Study of Constraint-Handling Techniques in the Optimal Synthesis of Mechanisms for Rehabilitation

2021 ◽  
Vol 11 (18) ◽  
pp. 8739
Author(s):  
José Saúl Muñoz-Reina ◽  
Miguel Gabriel Villarreal-Cervantes ◽  
Leonel Germán Corona-Ramírez
Keyword(s):  
Empirical Study ◽  
Performance Metrics ◽  
Low Cost ◽  
Kinematic Chain ◽  
Search Space ◽  
Metaheuristic Algorithms ◽  
Constraint Handling ◽  
Dimensional Synthesis ◽  
Constrained Problems ◽  
Four Bar Linkage

Currently, rehabilitation systems with closed kinematic chain mechanisms are low-cost alternatives for treatment and health care. In designing these systems, the dimensional synthesis is commonly stated as a constrained optimization problem to achieve repetitive rehabilitation movements, and metaheuristic algorithms for constrained problems are promising methods for searching solutions in the complex search space. The Constraint Handling Techniques (CHTs) in metaheuristic algorithms have different capacities to explore and exploit the search space. However, the study of the relationship in the CHT performance of the mechanism dimensional synthesis for rehabilitation systems has not been addressed, resulting in an important gap in the literature of such problems. In this paper, we present a comparative empirical study to investigate the influence of four CHTs (penalty function, feasibility rules, stochastic-ranking, and ϵ-constraint) on the performance of ten representative algorithms that have been reported in the literature for solving mechanism synthesis for rehabilitation (four-bar linkage, eight-bar linkage, and cam-linkage mechanisms). The study involves analysis of the overall performance, six performance metrics, and evaluation of the obtained mechanism. This identified that feasibility rules usually led to efficient optimization for most analyzed algorithms and presented more consistency of the obtained results in these kinds of problems.

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

2020 ◽  
pp. 1573-1593
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul ◽  
Heyam H. Al-Baity
Keyword(s):  
Swarm Intelligence ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Search Space ◽  
Constraint Handling ◽  
Nsga Ii ◽  
Multi Objective ◽  
Handling Mechanism ◽  
Pareto Fronts

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

On the Convergence and Diversity of Pareto Fronts Using Swarm Intelligence Metaheuristics for Constrained Search Space

2018 ◽  
Vol 9 (1) ◽  
pp. 20-38
Author(s):  
Kamel Zeltni ◽  
Souham Meshoul ◽  
Heyam H. Al-Baity
Keyword(s):  
Swarm Intelligence ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Search Space ◽  
Constraint Handling ◽  
Test Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Pareto Fronts

This article reviews existing constraint-handling techniques then presents a new design for Swarm Intelligence Metaheuristics (SIM) to deal with constrained multi-objective optimization problems (CMOPs). This new design aims to investigate potential effects of leader concepts that characterize the dynamic of SIM in the hope to help the population to reach Pareto optimal solutions in a constrained search space. The new leader-based constraint handling mechanism is incorporated in Constrained Multi-Objective Cuckoo Search (C-MOCS) and Constrained Multi-Objective Particle Swarm Optimization (C-MOPSO) as specific instances of a more general class of SIMs. The experimental results are carried out using a set of six well-known test functions and two performance metrics. The convergence and diversity of C-MOCS and C-MOPSO are analysed and compared to the well-known Multi-Objective Evolutionary Algorithm (MOEA) NSGA-II and discussed based on the obtained results.

scholarly journals A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1179
Author(s):  
Carolina Del-Valle-Soto ◽  
Carlos Mex-Perera ◽  
Juan Arturo Nolazco-Flores ◽  
Alma Rodríguez ◽  
Julio C. Rosas-Caro ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Performance Metrics ◽  
Low Cost ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
Wireless Sensor ◽  
Jamming Detection ◽  
Minimal Energy Expenditure

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

Towards Faster Mining of Disjunction-Based Concise Representations of Frequent Patterns

2014 ◽  
Vol 23 (02) ◽  
pp. 1450001
Author(s):  
T. Hamrouni ◽  
S. Ben Yahia ◽  
E. Mephu Nguifo
Keyword(s):  
Empirical Study ◽  
Real Life ◽  
Search Space ◽  
Frequent Patterns ◽  
Memory Consumption ◽  
Efficient Tool ◽  
Condensed Representation ◽  
Benchmark Datasets ◽  
Condensed Representations ◽  
Amount Of Knowledge

In many real-life datasets, the number of extracted frequent patterns was shown to be huge, hampering the effective exploitation of such amount of knowledge by human experts. To overcome this limitation, exact condensed representations were introduced in order to offer a small-sized set of elements from which the faithful retrieval of all frequent patterns is possible. In this paper, we introduce a new exact condensed representation only based on particular elements from the disjunctive search space. In this space, a pattern is characterized by its disjunctive support, i.e., the frequency of complementary occurrences – instead of the ubiquitous co-occurrence link – of its items. For several benchmark datasets, this representation has been shown interesting in compactness terms compared to the pioneering approaches of the literature. In this respect, we mainly focus here on proposing an efficient tool for mining this representation. For this purpose, we introduce an algorithm, called DSSRM, dedicated to this task. We also propose several techniques to optimize its mining time as well as its memory consumption. The carried out empirical study on benchmark datasets shows that DSSRM is faster by several orders of magnitude than the MEP algorithm.

Low-cost Indirect Measurement Methods for Self-x Integrated Sensory Electronics for Industry 4.0

2020 ◽  
Vol 87 (s1) ◽  
pp. s79-s84
Author(s):  
Qummar Zaman ◽  
Senan Alraho ◽  
Andreas König
Keyword(s):  
Industry 4.0 ◽  
Degrees Of Freedom ◽  
Performance Metrics ◽  
Low Cost ◽  
Indirect Measurement ◽  
Measurement Methods ◽  
Instrumentation Amplifier ◽  
Performance Parameters ◽  
Current Feedback ◽  
Indirect Measurements

AbstractThe conventional method for testing the performance of reconfigurable sensory electronics of industry 4.0 relies on the direct measurement methods. This approach gives higher accuracy but at the price of extremely high testing cost and does not utilize the new degrees of freedom for measurement methods enabled by industry 4.0. In order to reduce the test cost and use available resources more efficiently, a primary approach, called indirect measurements or alternative testing has been proposed using a non-intrusive sensor. Its basic principle consists in using the indirect measurements, in order to estimate the sensory electronics performance parameters without measuring directly. The non-intrusive property of the proposed method offers better performance of the sensing electronics and virtually applicable to any sensing electronics. Efficiency is evaluated in terms of model accuracy by using six different classical metrics. It uses an indirect current-feedback instrumentation amplifier (InAmp) as a test vehicle to evaluate the performance parameters of the circuit. The device is implemented using CMOS 0.35 μm technology. The achieved maximum value of average expected error metrics is 0.24, and the lowest value of correlation performance metrics is 0.91, which represent an excellent efficiency of InAmp performance predictor.

Synthesis and Balancing of Cam-Modulated Linkages

1987 ◽  
Author(s):  
P. Pracht ◽  
P. Minotti ◽  
M. Dahan
Keyword(s):  
Systematic Approach ◽  
Kinematic Chain ◽  
Path Generation ◽  
Industrial Manipulator ◽  
High Speeds ◽  
Structural Error ◽  
Mass Balancing ◽  
Four Bar Linkage ◽  
Robotic Applications ◽  
Class Of Functions

Abstract Linkages are inherently light, inexpensive, strong, adaptable to high speeds and have little friction. Moreover the class of functions suitable for linkage representation is large. For all these reasons numerous recent works deal with the problem of design mechanisms for robotic applications, but very often in terms of components such as gripper, transmission, balancing. We investigate a new application for linkages, using them to design industrial manipulator. The selected mechanism for this application is a four bar linkage with an adjustable lengh for exact path generation. This adjustment is performed by a track or cam which is substituted to a bar. By this mean, we define a cam-modulated linkage which possess superior accuracy potential and is capable of accomodating of industrial design restrictions. Such a kinematic chain is free from structural error for path generation and the presence of the track introduces the flexibility and versality in the usefull four bar chain. The synthesis technique of cam modulated linkage utilizes loop closure equations, envelop theory to find the centerline and the profile of the track. These techniques provide a systematic approach to the design of mechanism for path generation when extreme accuracy is required. In order to complete an contribution, we take in consideration the static balancing of the synthesized manipulator. To achieve static mass balancing we use the potential energy storage capabilities of linear springs, and integrated it with the non-linear motion of mechanism to provide an exact value of the desired counter loading functions. Examples are worked to demonstrate applications of these procedures and to illustrate the industrial potential of spring balancing and cam-modulated linkage.

scholarly journals Supermarket Sales Prediction Using Regression

2021 ◽  
Vol 10 (2) ◽  
pp. 1153-1157
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Customer Data ◽  
Sales Data ◽  
Online Marketplace ◽  
Sales Prediction ◽  
The Future

Sales forecasting is an important when it comes to companies who are engaged in retailing, logistics, manufacturing, marketing and wholesaling. It allows companies to allocate resources efficiently, to estimate revenue of the sales and to plan strategies which are better for company’s future. In this paper, predicting product sales from a particular store is done in a way that produces better performance compared to any machine learning algorithms. The dataset used for this project is Big Mart Sales data of the 2013.Nowadays shopping malls and Supermarkets keep track of the sales data of the each and every individual item for predicting the future demand of the customer. It contains large amount of customer data and the item attributes. Further, the frequent patterns are detected by mining the data from the data warehouse. Then the data can be used for predicting the sales of the future with the help of several machine learning techniques (algorithms) for the companies like Big Mart. In this project, we propose a model using the Xgboost algorithm for predicting sales of companies like Big Mart and founded that it produces better performance compared to other existing models. An analysis of this model with other models in terms of their performance metrics is made in this project. Big Mart is an online marketplace where people can buy or sell or advertise your merchandise at low cost. The goal of the paper is to make Big Mart the shopping paradise for the buyers and a marketing solutions for the sellers as well. The ultimate aim is the complete satisfaction of the customers. The project “SUPERMARKET SALES PREDICTION” builds a predictive model and finds out the sales of each of the product at a particular store. The Big Mart use this model to under the properties of the products which plays a major role in increasing the sales. This can also be done on the basis hypothesis that should be done before looking at the data

scholarly journals Enhancement of WiMAX networks using OPNET modeler platform

2021 ◽  
Vol 23 (3) ◽  
pp. 1510
Author(s):  
Noor Nateq Alfaisaly ◽  
Suhad Qasim Naeem ◽  
Azhar Hussein Neama
Keyword(s):  
High Speed ◽  
Network Performance ◽  
Performance Metrics ◽  
Channel Model ◽  
Low Cost ◽  
Base Station ◽  
Multipath Channel ◽  
Coverage Area ◽  
Wimax Networks ◽  
Better Than

Worldwide interoperability microwave access (WiMAX) is an 802.16 wireless standard that delivers high speed, provides a data rate of 100 Mbps and a coverage area of 50 km. Voice over internet protocol (VoIP) is flexible and offers low-cost telephony for clients over IP. However, there are still many challenges that must be addressed to provide a stable and good quality voice connection over the internet. The performance of various parameters such as multipath channel model and bandwidth over the Star trajectoryWiMAX network were evaluated under a scenario consisting of four cells. Each cell contains one mobile and one base station. Network performance metrics such as throughput and MOS were used to evaluate the best performance of VoIP codecs. Performance was analyzed via OPNET program14.5. The result use of multipath channel model (disable) was better than using the model (ITU pedestrian A). The value of the throughput at 15 dB was approximately 1600 packet/sec, and at -1 dB was its value 1300 packet/se. According to data, the Multipath channel model of the disable type the value of the MOS was better than the ITU Pedestrian A type.

scholarly journals Gaussian Process Regression Based Robust Optimization with Observer Uncertainty for Reconfigurable Self-x Sensory Electronics for Industry 4.0

2021 ◽  
Vol 88 (s1) ◽  
pp. s83-s88
Author(s):  
Qummar Zaman ◽  
Senan Alraho ◽  
Andreas König
Keyword(s):  
Robust Optimization ◽  
Gaussian Process ◽  
Industry 4.0 ◽  
Performance Metrics ◽  
Optimization Technique ◽  
Gaussian Process Regression ◽  
Search Space ◽  
Instrumentation Amplifier ◽  
Statistical Regression ◽  
Current Feedback

Abstract This paper presents a robust optimization technique for the reconfigurable measurement of sensory electronics for industry 4.0 to obtain a robust solution even in the presence of observer uncertainty using a cost-effective performance measurement method. The extrinsic evaluation of the proposed methodology is performed on an indirect current-feedback instrumentation amplifier (CFIA), which is a fundamental part of sensory systems. To reduce the CFIA device performance evaluation set-up cost, a low-cost test stimulus is applied to the circuit under test, and the output response of the circuit is examined to correlate with the device’s performance parameters. Due to the complexity of the smart sensory electronics search space, the meta-heuristic optimization algorithm is being selected as an optimizer. For objective space or observer uncertainty, the Gaussian process regression from the Bayesian statistical regression process is used to estimate the uncertainty level efficiently. Six different classical metrics have been used to evaluate the regression model accuracy. The highest achieved average expected error metrics value is 0.313, and the minimum value of correlation performance metrics is 0.908. The device is implemented using 0.35 μm austriamicrosystems technology.

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